Tackling Corruption Risks Linked to Politically Exposed Persons: 6th FALCON Policy Brief

This FALCON Policy Brief examines how inconsistent implementation and fragmented enforcement of existing EU anti-corruption and anti-money laundering (AML) frameworks limit effective detection and prevention of corruption involving politically exposed persons (PEPs). Fragmented data environments and limited interoperability—combined with increasingly complex financial structures—make it difficult to identify illicit activities, hidden relationships, and conflicts of interest.

Building on evidence from policy analysis and case studies, the brief highlights that PEPs and politically connected actors exploit discretionary decision-making processes, opaque ownership structures, and cross-border networks to gain illicit advantages. Companies linked to PEPs are more likely to be associated with high-risk tenders and elevated corruption risks, underlining the importance of political exposure as a key risk indicator.

Against this backdrop, FALCON proposes a set of targeted recommendations to strengthen EU-wide capabilities for detecting and combating corruption involving PEPs. These include improvements in data integration and interoperability, stronger regulatory frameworks, and the use of AI-driven analytics to detect unexplained wealth, conflicts of interest, and hidden ownership structures.

Policy recommendations

Improving data for anti-corruption and PEP risk detection:

• Address fragmented, non-standardised datasets across governance domains
• Enable interoperability between company, procurement, ownership, sanctions, and asset data
• Expand secure cross-border data sharing among competent authorities
• Use integrated datasets to identify hidden relationships and high-risk patterns

Strengthening the PEP anti-corruption legal framework:

• Reduce discretionary procedures in politically sensitive decision-making
• Strengthen oversight and transparency in public procurement and appointments
• Harmonise and enforce comprehensive conflict-of-interest disclosure rules
• Address risks from intermediaries, family networks, and opaque ownership structures

Integrating AI and machine learning tools for detection:

• Combine procurement, financial, ownership, and PEP data for advanced analysis
• Detect unexplained wealth, conflicts of interest, and suspicious transaction patterns
• Support prioritisation of high-risk investigations through data-driven insights
• Ensure alignment with EU AI Act, GDPR, and ethical AI principles, including human oversight

Overall, the brief demonstrates that strengthening interoperability, improving oversight mechanisms, and leveraging AI-supported analytics can significantly enhance the EU’s capacity to detect corruption risks involving PEPs at an early stage. A more harmonised, data-driven, and accountable framework would not only improve enforcement but also reinforce transparency, institutional resilience, and trust in democratic governance.